Data mining for energy efficient automotive factories

Energy is increasingly crucial for industries, especially with rising energy prices and the ur-gent need for sustainable practices. The industrial sector, accounting for over 22% of Europe's emissions, is pivotal in addressing global warming in line with the Paris Agreement's goals. This is particularly relevant in the automotive industry, where significant energy demand occurs through its large global scale. The European Union's "Energy Efficiency First" principle under the Green Deal aims to decarbonize energy systems by 2050, pushing industries like automotive manufacturing to enhance energy efficiency. Addressing the "energy efficiency gap"- the dis-parity between current practices and potential energy savings - requires innovative data-driven approaches to optimize energy use and close this gap effectively. However, the literature review reveals gaps in energy performance analysis, often focusing only on specific processes and not the entire system, leading to only local improvements and problem shifting. There is also a lack of detailed quantification of the energy performance gap and the consideration of competitor evaluation based on public data. Existing methods do not adequately address the allocation of energy gap causes across different measures and lack ad-vanced methodologies that integrate both white-box and black-box approaches. Additionally, there is a need to enhance the development of energy performance indicators for complex sys-tems and to improve methods for clustering similar systems for effective benchmarking. The research also identifies a need for more robust data-driven evaluation approaches to handle data interdependencies and non-linearities, and for continuous, coherent methodologies that support root-cause analysis to identify energy savings opportunities. This work introduces a novel and robust framework designed to enhance energy performance across automotive manufacturing systems. Grounded in cybernetic control loop thinking and data mining, it assesses and quantifies energy performance and identifies the most promising areas for improvement. By integrating this framework within the ISO 50001 standard, it ensures broad applicability and adherence to international energy management practices. The applica-tion of this framework has been validated through three detailed use cases, covering the three production management horizons. The first strategical use case focused on improving energy performance through internal and external benchmarking, highlighting the ability to predict energy performance on public available data. The second tactical use case utilized the frame-work to identify and implement energy performance improvement actions (EPIAs), using the systematic top-down decomposition method. The third operational use case showcased the framework's practical application in automotive paint shops, validating the effectiveness of au-tomated activity state labeling for monitoring performance changes using high-resolution time series data. These use cases not only demonstrated the framework's robustness and applicability but also enhancing its broader adoption potential to other industries and even to external organ-isations, governmental bodies, NGOs etc.